{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b09ea344",
   "metadata": {},
   "source": [
    "# 初步数据分析:\n",
    "包含12列数据，除了passengerId与Survived，还有10个特征。其中完整特征有7类，不完整特征3类(Age, Cabin, Embarked)\n",
    "- 性别分布: 男性占65%，女性占35%\n",
    "- 年龄分布: 范围从0.42到80岁，但有20%的数据缺失\n",
    "- 家庭关系:\n",
    "    - SibSp(兄弟姐妹/配偶数): 大多数为0，最高达8\n",
    "    - Parch(父母/子女数): 大多数为0，最高达6\n",
    "- 票价分布: 范围从0到512.33，无缺失值\n",
    "- 舱位信息: Cabin列缺失率高达77%，只有147个唯一值\n",
    "- 登船港口: S港72%，C港19%，Q港9%\n",
    "\n",
    "# 完整类数据处理(7类剔除2类)\n",
    "- ticket:票编号，与价格存在关联,编号相同的人之间存在关联，暂时剔除。\n",
    "- Name：名字，之间可能存在人员关系，用于其他信息的补充，最右进行训练时删除。\n",
    "- pclass: 票等级，使用独热编码处理。\n",
    "- fare: 票价，最大最小值归一化。\n",
    "- sex: 性别，硬编码处理。\n",
    "- sibsp: 兄弟姐妹/配偶数，使用独热编码处理。\n",
    "- parch: 父母/子女数，使用独热编码处理。\n",
    "\n",
    "\n",
    "# 不完整类数据处理\n",
    "- Cabin:舱位\n",
    "    - 缺失值处理：暂时剔除。可以将不同的人关联起来。\n",
    "- Age: 年龄\n",
    "    - 缺失值处理：使用众数/中位数填充。进一步可用姓名关联进行分析。\n",
    "    - 使用独热编码处理。\n",
    "- Embarked：登船港口\n",
    "    - 缺失值处理：使用中位数处理--进一步通过名字，票编号等管理补充。\n",
    "    - 使用独热编码处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9e31747d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 使用 pandas 读取 ===\n",
      "\n",
      "只读取指定列:\n",
      "   Pclass                                               Name     Sex   Age  \\\n",
      "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
      "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
      "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
      "\n",
      "   SibSp  Parch            Ticket     Fare Embarked  \n",
      "0      1      0         A/5 21171   7.2500        S  \n",
      "1      1      0          PC 17599  71.2833        C  \n",
      "2      0      0  STON/O2. 3101282   7.9250        S  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "\n",
    "\"\"\"使用 pandas 读取 CSV\"\"\"\n",
    "print(\"=== 使用 pandas 读取 ===\")\n",
    "# 读取全部数据\n",
    "current_dir = os.getcwd()\n",
    "# 构建train.csv的完整路径\n",
    "train_csv_path = os.path.join(current_dir, 'data/train.csv')\n",
    "test_csv_path = os.path.join(current_dir, 'data/test.csv')\n",
    "\n",
    "\n",
    "df_selected = pd.read_csv(train_csv_path, usecols=['Pclass', 'Name','Sex','SibSp','Parch','Ticket','Fare', 'Age','Embarked'])\n",
    "df_tag = pd.read_csv(train_csv_path, usecols=['Survived'])\n",
    "\n",
    "df_selected_test = pd.read_csv(test_csv_path, usecols=['Pclass', 'Name','Sex','SibSp','Parch','Ticket','Fare', 'Age','Embarked'])\n",
    "print(\"\\n只读取指定列:\")\n",
    "print(df_selected.head(3))\n",
    "\n",
    "df_transNum = df_selected\n",
    "df_transNum['Sex'] = df_selected['Sex'].map({'male':0, 'female':1})\n",
    "\n",
    "# 测试集处理\n",
    "df_transNum_test = df_selected_test\n",
    "df_transNum_test['Sex'] = df_selected_test['Sex'].map({'male':0, 'female':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b589babf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name_Class_for_Age\n",
      "0    521\n",
      "2    185\n",
      "3    127\n",
      "1     40\n",
      "5      9\n",
      "4      9\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Age缺失值补充-按人群分类后进行分类补充\n",
    "# 按Mr,Master,Miss,Mrs,Mme,Ms,Hg高等教育,Other进行人员分类。\n",
    "import re\n",
    "Name_KeyWord={r\"Mr\\.\",r\"Master\\.\",r\"Miss\\.\",r\"Mlle\\.\",r\"Mrs\\.\",r\"Mme\\.\",r\"Mrs \",r\"Ms\\.\",r\"Major\\.\",r\"Dr\\.\",r\"Rev\\.\",r\"Col\\.\",r\"Capt\\.\"}\n",
    "\n",
    "def get_name_class(name):\n",
    "      if pd.isna(name):\n",
    "        return 0  # 或其他默认值\n",
    "      if re.search(r\"\\bMr\\.\", str(name)):\n",
    "            return 0 \n",
    "      elif re.search(r\"\\bMaster\\.\", str(name)):\n",
    "            return 1 \n",
    "      elif re.search(r\"\\bMiss\\.|Mlle\\.|Ms\\.\", str(name)) :\n",
    "            return 2\n",
    "      elif re.search(r\"\\bMrs\\.|Mme\\.|Mrs \", str(name)) :\n",
    "            return 3\n",
    "      elif re.search(r\"\\bMajor\\.|Dr\\.\", str(name)) :\n",
    "            return 4\n",
    "      elif re.search(r\"\\bRev\\.|Col\\.|Capt\\.\", str(name)):\n",
    "            return 5\n",
    "      else:\n",
    "            return 0  # 默认分类\n",
    "\n",
    "df_transNum[\"Name_Class_for_Age\"] = df_transNum[\"Name\"].apply(get_name_class)\n",
    "age_median_by_class = df_transNum.groupby(\"Name_Class_for_Age\")[\"Age\"].median()\n",
    "df_transNum['Age'] = df_transNum.groupby(\"Name_Class_for_Age\")[\"Age\"].transform(lambda x: x.fillna(x.median()))\n",
    "print(df_transNum[\"Name_Class_for_Age\"].value_counts())\n",
    "\n",
    "# 测试集处理使用原数据集中位数与众数\n",
    "df_transNum_test[\"Name_Class_for_Age\"] = df_transNum_test[\"Name\"].apply(get_name_class)\n",
    "df_transNum_test['Age'] = df_transNum_test['Age'].fillna(\n",
    "    df_transNum_test['Name_Class_for_Age'].map(age_median_by_class)\n",
    ")\n",
    "\n",
    "# Embarked缺失值处理\n",
    "Embarked_mode = df_selected['Embarked'].dropna().mode()\n",
    "df_transNum['Embarked'] = df_transNum['Embarked'].fillna(Embarked_mode.values[0])\n",
    "df_transNum_test['Embarked'] = df_transNum_test['Embarked'].fillna(Embarked_mode.values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ef9b53",
   "metadata": {},
   "source": [
    "# 数据编码\n",
    "- Pclass: 复用1，2，3\n",
    "- Sex: male=0, female=1\n",
    "- SibSp: 复用原值\n",
    "- Parch: 复用原值\n",
    "- Fare: 复用票价\n",
    "- Age: 复用原值\n",
    "- Embarked: 热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6baf2bb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "创建了 239 个姓氏分类特征\n",
      "部分姓氏分类特征: ['Surname_Class_Abbott', 'Surname_Class_Abelseth', 'Surname_Class_Abelson', 'Surname_Class_Aks', 'Surname_Class_Ali', 'Surname_Class_Allen', 'Surname_Class_Allison', 'Surname_Class_Andersson', 'Surname_Class_Andrew', 'Surname_Class_Andrews']\n"
     ]
    }
   ],
   "source": [
    "# 按姓名增添加one-hot编码的家庭组特征\n",
    "def extract_surname(name):\n",
    "    \"\"\"\n",
    "    从姓名中提取姓氏。\n",
    "    假设姓氏是第一个空格前的部分，且不包含称谓。\n",
    "    \"\"\"   \n",
    "    if pd.isna(name):\n",
    "        return \"Unknown\"\n",
    "    # 提取第一个字段作为姓氏（处理如 \"van Houten\" 的情况可能需要更复杂的逻辑）\n",
    "    surname = name.strip().split(',')[0].strip()\n",
    "    return surname if surname else \"Unknown\"\n",
    "\n",
    "\n",
    "# 从原始数据中提取姓氏\n",
    "df_transNum['Surname'] = df_transNum['Name'].apply(extract_surname)\n",
    "df_transNum_test['Surname'] = df_transNum_test['Name'].apply(extract_surname)\n",
    "\n",
    "# 统计所有姓氏的出现频率（包括训练集和测试集）\n",
    "all_surnames_combined = pd.concat([df_transNum['Surname'], df_transNum_test['Surname']])\n",
    "surname_counts = all_surnames_combined.value_counts()\n",
    "# 定义分类函数\n",
    "def classify_surname(surname):\n",
    "    \"\"\"\n",
    "    对姓氏进行分类：\n",
    "    - 频繁出现的姓氏（出现次数>1）保持原样\n",
    "    - 只出现一次的姓氏归为\"Rare_Surname\"类\n",
    "    \"\"\"\n",
    "    if surname_counts[surname] > 1:\n",
    "        return surname\n",
    "    else:\n",
    "        return \"Rare_Surname\"\n",
    "    \n",
    "# 对姓氏进行分类\n",
    "df_transNum['Surname_Class'] = df_transNum['Surname'].apply(classify_surname)\n",
    "df_transNum_test['Surname_Class'] = df_transNum_test['Surname'].apply(classify_surname)\n",
    "# 获取所有可能的姓氏分类\n",
    "all_surname_classes = set(df_transNum['Surname_Class'].unique()) | set(df_transNum_test['Surname_Class'].unique())\n",
    "all_surname_classes = sorted(list(all_surname_classes))\n",
    "\n",
    "# 创建从姓氏分类到索引的映射\n",
    "surname_class_to_index = {surname_class: idx for idx, surname_class in enumerate(all_surname_classes)}\n",
    "\n",
    "# 为训练集和测试集创建统一的one-hot编码\n",
    "# 首先创建一个空的DataFrame，列名为所有可能的姓氏分类\n",
    "common_columns = [f'Surname_Class_{surname_class}' for surname_class in all_surname_classes]\n",
    "\n",
    "# 为训练集创建one-hot编码\n",
    "train_surname_class_indices = df_transNum['Surname_Class'].map(surname_class_to_index)\n",
    "train_surname_class_dummies = pd.DataFrame(0, index=df_transNum.index, columns=common_columns)\n",
    "for i, idx in enumerate(train_surname_class_indices):\n",
    "    train_surname_class_dummies.iloc[i, idx] = 1\n",
    "\n",
    "# 为测试集创建one-hot编码\n",
    "test_surname_class_indices = df_transNum_test['Surname_Class'].map(surname_class_to_index)\n",
    "test_surname_class_dummies = pd.DataFrame(0, index=df_transNum_test.index, columns=common_columns)\n",
    "for i, idx in enumerate(test_surname_class_indices):\n",
    "    test_surname_class_dummies.iloc[i, idx] = 1\n",
    "\n",
    "# 将one-hot编码添加到数据框中\n",
    "df_transNum = pd.concat([df_transNum.reset_index(drop=True), train_surname_class_dummies.reset_index(drop=True)], axis=1)\n",
    "df_transNum_test = pd.concat([df_transNum_test.reset_index(drop=True), test_surname_class_dummies.reset_index(drop=True)], axis=1)\n",
    "\n",
    "# 避免多重共线性问题 \n",
    "drop_column = common_columns[0]  # 删除第一个作为基准类别\n",
    "df_transNum = df_transNum.drop(columns=[drop_column])\n",
    "df_transNum_test = df_transNum_test.drop(columns=[drop_column])\n",
    "\n",
    "print(f\"创建了 {len(all_surname_classes)} 个姓氏分类特征\")\n",
    "print(\"部分姓氏分类特征:\", common_columns[:10])  # 显示前10个作为示例\n",
    "\n",
    "# 去除原始的Name、Surname和Surname_Class列\n",
    "df_transNum = df_transNum.drop(columns=['Name', 'Surname', 'Surname_Class'])\n",
    "df_transNum_test = df_transNum_test.drop(columns=['Name', 'Surname', 'Surname_Class'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ac6398a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总共创建了 3 个不同的Embarked\n",
      "总共创建了 3 个不同的Pclass\n"
     ]
    }
   ],
   "source": [
    "# 增加基于Embarked(三特征）的独热编码\n",
    "all_obj = set(df_transNum['Embarked'].unique())\n",
    "all_obj = sorted(list(all_obj)) \n",
    "obj_to_index = {obj: idx for idx, obj in enumerate(all_obj)}\n",
    "common_columns = [f'Embarked_{obj}' for obj in all_obj]\n",
    "print(f\"总共创建了 {len(all_obj)} 个不同的Embarked\")\n",
    "\n",
    "# 训练集生成并添加独热编码\n",
    "obj_indices = df_transNum['Embarked'].map(obj_to_index)\n",
    "obj_dummies = pd.DataFrame(0, index=df_transNum.index, columns=common_columns)\n",
    "for i, idx in enumerate(obj_indices):\n",
    "    obj_dummies.iloc[i, idx] = 1\n",
    "df_transNum = pd.concat([df_transNum.reset_index(drop=True), obj_dummies.reset_index(drop=True)], axis=1)\n",
    "\n",
    "# 测试集生成并添加独热编码\n",
    "obj_indices = df_transNum_test['Embarked'].map(obj_to_index)\n",
    "obj_dummies = pd.DataFrame(0, index=df_transNum_test.index, columns=common_columns)\n",
    "for i, idx in enumerate(obj_indices):\n",
    "    obj_dummies.iloc[i, idx] = 1\n",
    "df_transNum_test = pd.concat([df_transNum_test.reset_index(drop=True), obj_dummies.reset_index(drop=True)], axis=1)\n",
    "\n",
    "drop_column = common_columns[0]  # 删除第一个作为基准类别\n",
    "df_transNum = df_transNum.drop(columns=[drop_column])\n",
    "df_transNum_test = df_transNum_test.drop(columns=[drop_column])\n",
    "\n",
    "# 去除Embarked列\n",
    "df_transNum = df_transNum.drop(columns=['Embarked'])\n",
    "df_transNum_test = df_transNum_test.drop(columns=['Embarked'])\n",
    "\n",
    "# 增加基于Pclass(三特征）的独热编码\n",
    "all_obj = set(df_transNum['Pclass'].unique())\n",
    "all_obj = sorted(list(all_obj)) \n",
    "obj_to_index = {obj: idx for idx, obj in enumerate(all_obj)}\n",
    "common_columns = [f'Pclass_{obj}' for obj in all_obj]\n",
    "print(f\"总共创建了 {len(all_obj)} 个不同的Pclass\")\n",
    "# 训练集生成并添加独热编码\n",
    "obj_indices = df_transNum['Pclass'].map(obj_to_index)\n",
    "obj_dummies = pd.DataFrame(0, index=df_transNum.index, columns=common_columns)\n",
    "for i, idx in enumerate(obj_indices):\n",
    "    obj_dummies.iloc[i, idx] = 1\n",
    "df_transNum = pd.concat([df_transNum.reset_index(drop=True), obj_dummies.reset_index(drop=True)], axis=1)\n",
    "\n",
    "# 测试集生成并添加独热编码\n",
    "obj_indices = df_transNum_test['Pclass'].map(obj_to_index)\n",
    "obj_dummies = pd.DataFrame(0, index=df_transNum_test.index, columns=common_columns)\n",
    "for i, idx in enumerate(obj_indices):\n",
    "    obj_dummies.iloc[i, idx] = 1\n",
    "df_transNum_test = pd.concat([df_transNum_test.reset_index(drop=True), obj_dummies.reset_index(drop=True)], axis=1)\n",
    "\n",
    "drop_column = common_columns[0]  # 删除第一个作为基准类别\n",
    "df_transNum = df_transNum.drop(columns=[drop_column])\n",
    "df_transNum_test = df_transNum_test.drop(columns=[drop_column])\n",
    "\n",
    "# 去除Pclass列\n",
    "df_transNum = df_transNum.drop(columns=['Pclass'])\n",
    "df_transNum_test = df_transNum_test.drop(columns=['Pclass'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3208c46a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总共生成ticket_groups数量 427\n",
      "基于Ticket和Fare创建了 426 个独热编码特征\n",
      "特征示例: ['Ticket_Group_1', 'Ticket_Group_10', 'Ticket_Group_100', 'Ticket_Group_101', 'Ticket_Group_102']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 应用分类基于ticket和Fare进行分组\n",
    "def is_numeric_ticket(ticket):\n",
    "    \"\"\"判断票号是否为纯数字\"\"\"\n",
    "    if pd.isna(ticket):\n",
    "        return False\n",
    "    ticket_str = str(ticket).strip()\n",
    "    # 检查是否只包含数字和可能的分隔符\n",
    "    return bool(re.match(r'^[\\d\\s\\-/]+$', ticket_str))\n",
    "\n",
    "def extract_ticket_parts(ticket):\n",
    "    \"\"\"从非数字票号中提取字母前缀和数字部分\"\"\"\n",
    "    if pd.isna(ticket):\n",
    "        return \"\", 0\n",
    "    ticket_str = str(ticket).strip()\n",
    "    # 提取字母前缀\n",
    "    prefix = re.search(r'^[A-Za-z\\s]+', ticket_str)\n",
    "    prefix = prefix.group().strip() if prefix else \"\"\n",
    "    # 提取数字部分\n",
    "    numbers = re.findall(r'\\d+', ticket_str)\n",
    "    number = int(numbers[-1]) if numbers else 0\n",
    "    return prefix, number\n",
    "\n",
    "def tickets_match(ticket1, ticket2):\n",
    "    \"\"\"判断两个票号是否匹配\"\"\"\n",
    "    # 如果任一票号为空，不匹配\n",
    "    if pd.isna(ticket1) or pd.isna(ticket2):\n",
    "        return False\n",
    "    \n",
    "    ticket1_str = str(ticket1).strip()\n",
    "    ticket2_str = str(ticket2).strip()\n",
    "    \n",
    "    # 如果两个都是纯数字票号\n",
    "    if is_numeric_ticket(ticket1_str) and is_numeric_ticket(ticket2_str):\n",
    "        try:\n",
    "            num1 = int(re.findall(r'\\d+', ticket1_str)[-1]) if re.findall(r'\\d+', ticket1_str) else 0\n",
    "            num2 = int(re.findall(r'\\d+', ticket2_str)[-1]) if re.findall(r'\\d+', ticket2_str) else 0\n",
    "            return abs(num1 - num2) < 3\n",
    "        except:\n",
    "            return ticket1_str == ticket2_str\n",
    "    \n",
    "    # 如果两个都是非数字票号\n",
    "    elif not is_numeric_ticket(ticket1_str) and not is_numeric_ticket(ticket2_str):\n",
    "        prefix1, num1 = extract_ticket_parts(ticket1_str)\n",
    "        prefix2, num2 = extract_ticket_parts(ticket2_str)\n",
    "        # 前半段字母相同，后半段数字相减小于3\n",
    "        return prefix1 == prefix2 and abs(num1 - num2) < 3\n",
    "    \n",
    "    # 其他情况（一个数字一个非数字）不匹配\n",
    "    else:\n",
    "        return False\n",
    "    \n",
    "temp_train = pd.DataFrame({\n",
    "        'fare': df_transNum['Fare'].values,\n",
    "        'dataType':\"train\",\n",
    "        'ticket_full': df_transNum['Ticket'].values\n",
    "    })\n",
    "\n",
    "temp_test = pd.DataFrame({\n",
    "        'fare': df_transNum_test['Fare'].values,\n",
    "        'dataType':\"test\",\n",
    "        'ticket_full': df_transNum_test['Ticket'].values\n",
    "    })  \n",
    "temp_data = pd.concat([temp_train, temp_test],ignore_index=True)\n",
    "temp_data['ticket_groups'] = '0'\n",
    "group_id = 0\n",
    "for index in range(len(temp_data) - 1) :\n",
    "    ticket_name = temp_data.iloc[index]['ticket_full']\n",
    "    ticket_fare = temp_data.iloc[index]['fare']\n",
    "    # 检查是否未分组\n",
    "    if temp_data.iloc[index]['ticket_groups'] == '0':  # 未分组\n",
    "        for index_in in range(index + 1, len(temp_data)):\n",
    "            if temp_data.iloc[index]['ticket_groups'] != '0':  # 未分组\n",
    "                continue\n",
    "            compare_ticket = temp_data.iloc[index_in]['ticket_full']\n",
    "            compare_fare = temp_data.iloc[index_in]['fare']\n",
    "\n",
    "            if tickets_match(ticket_name, compare_ticket) and abs(ticket_fare - compare_fare) == 0:\n",
    "                group_name = f'Ticket_Group_{int(group_id)}'\n",
    "                temp_data.loc[index,'ticket_groups'] = group_name\n",
    "                temp_data.loc[index_in,'ticket_groups'] = group_name\n",
    "                group_id += 1\n",
    "temp_data.loc[temp_data['ticket_groups'] == '0', 'ticket_groups'] = 'Ticket_Group_other'\n",
    "print(f'总共生成ticket_groups数量 {group_id+1}')\n",
    "                \n",
    "            \n",
    "# 基于Ticket_Group创建独热编码\n",
    "ticket_groups_dummies = pd.get_dummies(temp_data['ticket_groups'], dtype=int)\n",
    "ticket_groups_dummies.columns = [f'{col}' for col in ticket_groups_dummies.columns]\n",
    "\n",
    "# 分离训练集和测试集的独热编码\n",
    "train_mask = temp_data['dataType'] == 'train'\n",
    "test_mask = temp_data['dataType'] == 'test'\n",
    "\n",
    "train_ticket_dummies = ticket_groups_dummies[train_mask].reset_index(drop=True)\n",
    "test_ticket_dummies = ticket_groups_dummies[test_mask].reset_index(drop=True)\n",
    "\n",
    "# 删除第一列以避免多重共线性问题\n",
    "if len(train_ticket_dummies.columns) > 1:\n",
    "    train_ticket_dummies = train_ticket_dummies.iloc[:, 1:]\n",
    "    test_ticket_dummies = test_ticket_dummies.iloc[:, 1:]\n",
    "\n",
    "# 添加到数据框\n",
    "df_transNum = pd.concat([df_transNum.reset_index(drop=True), train_ticket_dummies], axis=1)\n",
    "df_transNum_test = pd.concat([df_transNum_test.reset_index(drop=True), test_ticket_dummies], axis=1)\n",
    "\n",
    "print(f\"基于Ticket和Fare创建了 {len(train_ticket_dummies.columns)} 个独热编码特征\")\n",
    "print(f\"特征示例: {list(train_ticket_dummies.columns[:5])}\")\n",
    "\n",
    "# 删除Ticket和Fare列\n",
    "df_transNum = df_transNum.drop(columns=['Ticket', 'Fare'])\n",
    "df_transNum_test = df_transNum_test.drop(columns=['Ticket', 'Fare'])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a340668",
   "metadata": {},
   "source": [
    "# 特征构造\n",
    "- ['Pclass', 'Sex','SibSp','Parch','Fare', 'Age','Embarked']\n",
    "- df_norm,df_norm_test\n",
    "- age5个区间\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d7ac20e",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[28]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# 年龄分区\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m age_bins = [\u001b[32m0\u001b[39m, \u001b[32m12\u001b[39m, \u001b[32m18\u001b[39m, \u001b[32m35\u001b[39m, \u001b[32m60\u001b[39m, \u001b[32m80\u001b[39m]\n\u001b[32m      3\u001b[39m age_labels = [\u001b[32m0\u001b[39m, \u001b[32m1\u001b[39m, \u001b[32m2\u001b[39m, \u001b[32m3\u001b[39m, \u001b[32m4\u001b[39m]\n\u001b[32m      4\u001b[39m df_transNum[\u001b[33m'\u001b[39m\u001b[33mAge_Group\u001b[39m\u001b[33m'\u001b[39m] = pd.cut(df_transNum[\u001b[33m'\u001b[39m\u001b[33mAge\u001b[39m\u001b[33m'\u001b[39m], bins = age_bins, labels = age_labels)\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[28]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# 年龄分区\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m age_bins = [\u001b[32m0\u001b[39m, \u001b[32m12\u001b[39m, \u001b[32m18\u001b[39m, \u001b[32m35\u001b[39m, \u001b[32m60\u001b[39m, \u001b[32m80\u001b[39m]\n\u001b[32m      3\u001b[39m age_labels = [\u001b[32m0\u001b[39m, \u001b[32m1\u001b[39m, \u001b[32m2\u001b[39m, \u001b[32m3\u001b[39m, \u001b[32m4\u001b[39m]\n\u001b[32m      4\u001b[39m df_transNum[\u001b[33m'\u001b[39m\u001b[33mAge_Group\u001b[39m\u001b[33m'\u001b[39m] = pd.cut(df_transNum[\u001b[33m'\u001b[39m\u001b[33mAge\u001b[39m\u001b[33m'\u001b[39m], bins = age_bins, labels = age_labels)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m_pydevd_bundle\\\\pydevd_cython.pyx:1697\u001b[39m, in \u001b[36m_pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m_pydevd_bundle\\\\pydevd_cython.pyx:634\u001b[39m, in \u001b[36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m_pydevd_bundle\\\\pydevd_cython.pyx:1112\u001b[39m, in \u001b[36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m_pydevd_bundle\\\\pydevd_cython.pyx:1090\u001b[39m, in \u001b[36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m_pydevd_bundle\\\\pydevd_cython.pyx:494\u001b[39m, in \u001b[36m_pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mf:\\myVenv\\kaggle\\Lib\\site-packages\\debugpy\\_vendored\\pydevd\\pydevd.py:2188\u001b[39m, in \u001b[36mPyDB.do_wait_suspend\u001b[39m\u001b[34m(self, thread, frame, event, arg, exception_type)\u001b[39m\n\u001b[32m   2185\u001b[39m             from_this_thread.append(frame_custom_thread_id)\n\u001b[32m   2187\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._threads_suspended_single_notification.notify_thread_suspended(thread_id, thread, stop_reason):\n\u001b[32m-> \u001b[39m\u001b[32m2188\u001b[39m         keep_suspended = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_do_wait_suspend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrace_suspend_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_this_thread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes_tracker\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2190\u001b[39m frames_list = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   2192\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m keep_suspended:\n\u001b[32m   2193\u001b[39m     \u001b[38;5;66;03m# This means that we should pause again after a set next statement.\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mf:\\myVenv\\kaggle\\Lib\\site-packages\\debugpy\\_vendored\\pydevd\\pydevd.py:2257\u001b[39m, in \u001b[36mPyDB._do_wait_suspend\u001b[39m\u001b[34m(self, thread, frame, event, arg, trace_suspend_type, from_this_thread, frames_tracker)\u001b[39m\n\u001b[32m   2254\u001b[39m                 queue.put(internal_cmd)\n\u001b[32m   2255\u001b[39m                 wait_timeout = TIMEOUT_FAST\n\u001b[32m-> \u001b[39m\u001b[32m2257\u001b[39m         \u001b[43mnotify_event\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwait_timeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2258\u001b[39m         notify_event.clear()\n\u001b[32m   2260\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.2544.0_x64__qbz5n2kfra8p0\\Lib\\threading.py:629\u001b[39m, in \u001b[36mEvent.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m    627\u001b[39m signaled = \u001b[38;5;28mself\u001b[39m._flag\n\u001b[32m    628\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m signaled:\n\u001b[32m--> \u001b[39m\u001b[32m629\u001b[39m     signaled = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_cond\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    630\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m signaled\n",
      "\u001b[36mFile \u001b[39m\u001b[32mC:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.2544.0_x64__qbz5n2kfra8p0\\Lib\\threading.py:331\u001b[39m, in \u001b[36mCondition.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m    329\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    330\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m timeout > \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m331\u001b[39m         gotit = \u001b[43mwaiter\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    332\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    333\u001b[39m         gotit = waiter.acquire(\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[31mKeyboardInterrupt\u001b[39m: "
     ]
    }
   ],
   "source": [
    "# 年龄分区\n",
    "age_bins = [0, 12, 18, 35, 60, 80]\n",
    "age_labels = [0, 1, 2, 3, 4]\n",
    "df_transNum['Age_Group'] = pd.cut(df_transNum['Age'], bins = age_bins, labels = age_labels)\n",
    "df_transNum['Age_Group'] = df_transNum['Age_Group'].astype(int)\n",
    "\n",
    "df_transNum_test['Age_Group'] = pd.cut(df_transNum_test['Age'], bins = age_bins, labels = age_labels)\n",
    "df_transNum_test['Age_Group'] = df_transNum_test['Age_Group'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ce17f5f",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Fare'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32m/home/media/py311Venv/kaggle/lib/python3.11/site-packages/pandas/core/indexes/base.py:3812\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3811\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3812\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   3813\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:167\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:196\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7096\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[31mKeyError\u001b[39m: 'Fare'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      2\u001b[39m bins = [-\u001b[32m1\u001b[39m,\u001b[32m30\u001b[39m,\u001b[32m150\u001b[39m,\u001b[32m400\u001b[39m,\u001b[32m600\u001b[39m]\u001b[38;5;66;03m#对于0的情况可能覆盖不到，所以设置-1为0\u001b[39;00m\n\u001b[32m      3\u001b[39m labels = [\u001b[32m0\u001b[39m,\u001b[32m1\u001b[39m,\u001b[32m2\u001b[39m,\u001b[32m3\u001b[39m]\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m训练集 Fare 列 NaN 数量:\u001b[39m\u001b[33m\"\u001b[39m, \u001b[43mdf_transNum\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mFare\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m.isnull().sum())\n\u001b[32m      5\u001b[39m df_transNum[\u001b[33m'\u001b[39m\u001b[33mFare_Group\u001b[39m\u001b[33m'\u001b[39m] = pd.cut(df_transNum[\u001b[33m'\u001b[39m\u001b[33mFare\u001b[39m\u001b[33m'\u001b[39m], bins=bins, labels=labels)\n\u001b[32m      6\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m训练集 Fare_Group 列 NaN 数量:\u001b[39m\u001b[33m\"\u001b[39m, df_transNum[\u001b[33m'\u001b[39m\u001b[33mFare_Group\u001b[39m\u001b[33m'\u001b[39m].isnull().sum())\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/home/media/py311Venv/kaggle/lib/python3.11/site-packages/pandas/core/frame.py:4113\u001b[39m, in \u001b[36mDataFrame.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   4111\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.columns.nlevels > \u001b[32m1\u001b[39m:\n\u001b[32m   4112\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._getitem_multilevel(key)\n\u001b[32m-> \u001b[39m\u001b[32m4113\u001b[39m indexer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   4114\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[32m   4115\u001b[39m     indexer = [indexer]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/home/media/py311Venv/kaggle/lib/python3.11/site-packages/pandas/core/indexes/base.py:3819\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3814\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   3815\u001b[39m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc.Iterable)\n\u001b[32m   3816\u001b[39m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[32m   3817\u001b[39m     ):\n\u001b[32m   3818\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[32m-> \u001b[39m\u001b[32m3819\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m   3820\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m   3821\u001b[39m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[32m   3822\u001b[39m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[32m   3823\u001b[39m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[32m   3824\u001b[39m     \u001b[38;5;28mself\u001b[39m._check_indexing_error(key)\n",
      "\u001b[31mKeyError\u001b[39m: 'Fare'"
     ]
    }
   ],
   "source": [
    "# 票价分区\n",
    "bins = [-1,30,150,400,600]#对于0的情况可能覆盖不到，所以设置-1为0\n",
    "labels = [0,1,2,3]\n",
    "print(\"训练集 Fare 列 NaN 数量:\", df_transNum['Fare'].isnull().sum())\n",
    "df_transNum['Fare_Group'] = pd.cut(df_transNum['Fare'], bins=bins, labels=labels)\n",
    "print(\"训练集 Fare_Group 列 NaN 数量:\", df_transNum['Fare_Group'].isnull().sum())\n",
    "df_transNum['Fare_Group'] = df_transNum['Fare_Group'].astype(int)\n",
    "\n",
    "df_transNum_test['Fare_Group'] = pd.cut(df_transNum_test['Fare'], bins=bins, labels=labels)\n",
    "df_transNum_test['Fare_Group'] = df_transNum_test['Fare_Group'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce07de0c",
   "metadata": {},
   "source": [
    "# 以上均为分区思维，其目的是降维。从结果来看，特征反而是太少了，尝试增加特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f10f8012",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 独身\n",
    "df_transNum['Alone'] = (df_transNum['SibSp'] + df_transNum['Parch']).apply(lambda x: 1 if x == 0 else 0)\n",
    "df_transNum_test['Alone'] = (df_transNum_test['SibSp'] + df_transNum_test['Parch']).apply(lambda x: 1 if x == 0 else 0)\n",
    "\n",
    "# 独身女性\n",
    "df_transNum['Alone_female'] = (df_transNum['Alone'] + df_transNum['Sex']).apply(lambda x: 1 if x == 0 else 0)\n",
    "df_transNum_test['Alone_female'] = (df_transNum_test['Alone'] + df_transNum_test['Sex']).apply(lambda x: 1 if x == 0 else 0)\n",
    "\n",
    "# 有父母的女性\n",
    "df_transNum['Parent_female'] = (df_transNum['Parch'] + df_transNum['Sex']).apply(lambda x: 1 if x == 0 else 0)\n",
    "df_transNum_test['Parent_female'] = (df_transNum_test['Parch'] + df_transNum_test['Sex']).apply(lambda x: 1 if x == 0 else 0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c3fffb9",
   "metadata": {},
   "source": [
    "# 数据归一化\n",
    "- 原始数据归一化\n",
    "- 训练数据归一化-使用原始数据的参数-暂不涉及超出原数据参数之外的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ddaec146",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总共 673 列特征\n",
      "其中 666 列为独热编码特征，不进行归一化处理\n",
      "其中 7 列需要归一化处理\n"
     ]
    }
   ],
   "source": [
    "df_max_min = df_transNum.agg(['max', 'min'])\n",
    "\n",
    "# 区分需要归一化的特征和独热编码特征\n",
    "# 假设独热编码特征是以'Surname_'开头的列，以及一些已知的分类特征\n",
    "one_hot_columns = [col for col in df_transNum.columns if (col.startswith('Surname_') or col.startswith('Embarked_') or col.startswith('Ticket_Group_'))]\n",
    "# 如果还有其他独热编码特征，可以继续添加到这个列表中\n",
    "\n",
    "# 需要归一化的特征（排除独热编码特征）\n",
    "columns_to_normalize = [col for col in df_transNum.columns if col not in one_hot_columns]\n",
    "\n",
    "# 对训练集进行归一化（仅对非独热编码特征）\n",
    "df_norm = df_transNum.copy()\n",
    "df_norm[columns_to_normalize] = df_transNum[columns_to_normalize].apply(lambda x: (x - x.min()) / (x.max() - x.min()))\n",
    "\n",
    "# 对训练集进行标准化（仅对非独热编码特征）\n",
    "df_std = df_transNum.copy()\n",
    "df_std[columns_to_normalize] = df_transNum[columns_to_normalize].apply(lambda x: (x - x.mean()) / x.std())\n",
    "\n",
    "# 测试集处理 - 仅对非独热编码特征进行归一化\n",
    "df_norm_test = df_transNum_test.copy()\n",
    "for column in columns_to_normalize:\n",
    "    col_min = df_max_min.loc['min', column]\n",
    "    col_max = df_max_min.loc['max', column]\n",
    "    df_norm_test[column] = (df_transNum_test[column] - col_min) / (col_max - col_min)\n",
    "\n",
    "print(f\"总共 {len(df_transNum.columns)} 列特征\")\n",
    "print(f\"其中 {len(one_hot_columns)} 列为独热编码特征，不进行归一化处理\")\n",
    "print(f\"其中 {len(columns_to_normalize)} 列需要归一化处理\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed928989",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.24825981 0.20355171 0.12994305 0.09664347 0.06397372 0.04144826\n",
      " 0.0234893  0.02281138 0.01825129 0.01603879]\n"
     ]
    }
   ],
   "source": [
    "# PCA分析 -各项占比差不多,无法降维\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(10)\n",
    "pca.fit(df_std)\n",
    "print(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d9d0ce1",
   "metadata": {},
   "source": [
    "# 进一步分析特征与标签的关联性\n",
    "- 每个特征值进行分离，每个特征值中的每一类与生存的关系。\n",
    "- 如性别单独分析，分析男性与女性分别生存的情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e544498",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 各特征与Survived关联性分析 ===\n",
      "\n",
      "Sex 特征与Survived的关联性:\n",
      "          生存率  样本数\n",
      "Sex               \n",
      "0.0  0.188908  577\n",
      "1.0  0.742038  314\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Age 特征与Survived的关联性:\n",
      "          生存率  样本数\n",
      "Age               \n",
      "0.000000  1.0    1\n",
      "0.003141  1.0    1\n",
      "0.004147  1.0    2\n",
      "0.005152  1.0    2\n",
      "0.006283  1.0    1\n",
      "...       ...  ...\n",
      "0.874340  0.0    2\n",
      "0.880623  0.0    1\n",
      "0.886906  0.0    2\n",
      "0.924604  0.0    1\n",
      "1.000000  1.0    1\n",
      "\n",
      "[89 rows x 2 columns]\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "SibSp 特征与Survived的关联性:\n",
      "            生存率  样本数\n",
      "SibSp               \n",
      "0.000  0.345395  608\n",
      "0.125  0.535885  209\n",
      "0.250  0.464286   28\n",
      "0.375  0.250000   16\n",
      "0.500  0.166667   18\n",
      "0.625  0.000000    5\n",
      "1.000  0.000000    7\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Parch 特征与Survived的关联性:\n",
      "               生存率  样本数\n",
      "Parch                  \n",
      "0.000000  0.343658  678\n",
      "0.166667  0.550847  118\n",
      "0.333333  0.500000   80\n",
      "0.500000  0.600000    5\n",
      "0.666667  0.000000    4\n",
      "0.833333  0.200000    5\n",
      "1.000000  0.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Name_Class_for_Age 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Name_Class_for_Age               \n",
      "0.0                 0.159309  521\n",
      "0.2                 0.575000   40\n",
      "0.4                 0.702703  185\n",
      "0.6                 0.795276  127\n",
      "0.8                 0.444444    9\n",
      "1.0                 0.111111    9\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Abelseth 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Abelseth               \n",
      "0                       0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Abelson 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Abelson               \n",
      "0                      0.383577  889\n",
      "1                      0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Aks 特征与Survived的关联性:\n",
      "                        生存率  样本数\n",
      "Surname_Class_Aks               \n",
      "0                  0.383146  890\n",
      "1                  1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Ali 特征与Survived的关联性:\n",
      "                        生存率  样本数\n",
      "Surname_Class_Ali               \n",
      "0                  0.384702  889\n",
      "1                  0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Allen 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Allen               \n",
      "0                    0.383577  889\n",
      "1                    0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Allison 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Allison               \n",
      "0                      0.384009  888\n",
      "1                      0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Andersson 特征与Survived的关联性:\n",
      "                              生存率  样本数\n",
      "Surname_Class_Andersson               \n",
      "0                        0.385488  882\n",
      "1                        0.222222    9\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Andrew 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Andrew              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Andrews 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Andrews               \n",
      "0                      0.383577  889\n",
      "1                      0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Angle 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Angle               \n",
      "0                    0.383146  890\n",
      "1                    1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Arnold-Franchi 特征与Survived的关联性:\n",
      "                                   生存率  样本数\n",
      "Surname_Class_Arnold-Franchi               \n",
      "0                             0.384702  889\n",
      "1                             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Asplund 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Asplund               \n",
      "0                      0.382187  887\n",
      "1                      0.750000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Astor 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Astor               \n",
      "0                    0.383146  890\n",
      "1                    1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Attalah 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Attalah               \n",
      "0                      0.384702  889\n",
      "1                      0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Backstrom 特征与Survived的关联性:\n",
      "                              生存率  样本数\n",
      "Surname_Class_Backstrom               \n",
      "0                        0.383577  889\n",
      "1                        0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Baclini 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Baclini              \n",
      "0                      0.38106  887\n",
      "1                      1.00000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Barbara 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Barbara               \n",
      "0                      0.384702  889\n",
      "1                      0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Baxter 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Baxter               \n",
      "0                     0.383577  889\n",
      "1                     0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Beane 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Beane               \n",
      "0                    0.382452  889\n",
      "1                    1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Becker 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Becker               \n",
      "0                     0.382452  889\n",
      "1                     1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Beckwith 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Beckwith               \n",
      "0                       0.382452  889\n",
      "1                       1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Betros 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Betros              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Bishop 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Bishop               \n",
      "0                     0.382452  889\n",
      "1                     1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Bonnell 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Bonnell               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Boulos 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Boulos               \n",
      "0                     0.385135  888\n",
      "1                     0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Bourke 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Bourke               \n",
      "0                     0.385135  888\n",
      "1                     0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Bowen 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Bowen              \n",
      "0                    0.38427  890\n",
      "1                    0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Bradley 特征与Survived的关联性:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            生存率  样本数\n",
      "Surname_Class_Bradley               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Braund 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Braund               \n",
      "0                     0.384702  889\n",
      "1                     0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Brown 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Brown               \n",
      "0                    0.382187  887\n",
      "1                    0.750000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Bryhl 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Bryhl              \n",
      "0                    0.38427  890\n",
      "1                    0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Buckley 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Buckley               \n",
      "0                      0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Burns 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Burns               \n",
      "0                    0.383146  890\n",
      "1                    1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Cacic 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Cacic               \n",
      "0                    0.384702  889\n",
      "1                    0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Caldwell 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Caldwell               \n",
      "0                       0.382452  889\n",
      "1                       1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Calic 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Calic               \n",
      "0                    0.384702  889\n",
      "1                    0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Canavan 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Canavan              \n",
      "0                      0.38427  890\n",
      "1                      0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Caram 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Caram              \n",
      "0                    0.38427  890\n",
      "1                    0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Cardeza 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Cardeza               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Carlsson 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Carlsson               \n",
      "0                       0.384702  889\n",
      "1                       0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Carr 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Carr               \n",
      "0                   0.383146  890\n",
      "1                   1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Carrau 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Carrau              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Carter 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Carter               \n",
      "0                     0.381921  885\n",
      "1                     0.666667    6\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Cavendish 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Cavendish              \n",
      "0                        0.38427  890\n",
      "1                        0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Chaffee 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Chaffee              \n",
      "0                      0.38427  890\n",
      "1                      0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Chambers 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Chambers               \n",
      "0                       0.382452  889\n",
      "1                       1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Chapman 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Chapman               \n",
      "0                      0.384702  889\n",
      "1                      0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Christy 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Christy               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Chronopoulos 特征与Survived的关联性:\n",
      "                                生存率  样本数\n",
      "Surname_Class_Chronopoulos              \n",
      "0                           0.38427  890\n",
      "1                           0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Clark 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Clark               \n",
      "0                    0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Clarke 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Clarke               \n",
      "0                     0.383146  890\n",
      "1                     1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Coleff 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Coleff               \n",
      "0                     0.384702  889\n",
      "1                     0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Collyer 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Collyer               \n",
      "0                      0.382883  888\n",
      "1                      0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Compton 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Compton               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Connolly 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Connolly               \n",
      "0                       0.383146  890\n",
      "1                       1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Cook 特征与Survived的关联性:\n",
      "                        生存率  样本数\n",
      "Surname_Class_Cook              \n",
      "0                   0.38427  890\n",
      "1                   0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Cor 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Surname_Class_Cor              \n",
      "0                  0.38427  890\n",
      "1                  0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Coutts 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Coutts               \n",
      "0                     0.382452  889\n",
      "1                     1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Cribb 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Cribb              \n",
      "0                    0.38427  890\n",
      "1                    0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Crosby 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Crosby               \n",
      "0                     0.383577  889\n",
      "1                     0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Cumings 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Cumings               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Daly 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Daly               \n",
      "0                   0.382452  889\n",
      "1                   1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Danbom 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Danbom               \n",
      "0                     0.384702  889\n",
      "1                     0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Davidson 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Davidson              \n",
      "0                       0.38427  890\n",
      "1                       0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Davies 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Davies               \n",
      "0                     0.384009  888\n",
      "1                     0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Davison 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Davison               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Dean 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Dean               \n",
      "0                   0.383577  889\n",
      "1                   0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Dennis 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Dennis              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Dick 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Dick               \n",
      "0                   0.382452  889\n",
      "1                   1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Dodge 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Dodge               \n",
      "0                    0.383146  890\n",
      "1                    1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Doling 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Doling               \n",
      "0                     0.382452  889\n",
      "1                     1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Douglas 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Douglas              \n",
      "0                      0.38427  890\n",
      "1                      0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Drew 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Drew               \n",
      "0                   0.383146  890\n",
      "1                   1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Duff Gordon 特征与Survived的关联性:\n",
      "                                生存率  样本数\n",
      "Surname_Class_Duff Gordon               \n",
      "0                          0.382452  889\n",
      "1                          1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Duran y More 特征与Survived的关联性:\n",
      "                                 生存率  样本数\n",
      "Surname_Class_Duran y More               \n",
      "0                           0.383146  890\n",
      "1                           1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Dyker 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Dyker               \n",
      "0                    0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Elias 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Elias               \n",
      "0                    0.385135  888\n",
      "1                    0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Faunthorpe 特征与Survived的关联性:\n",
      "                               生存率  样本数\n",
      "Surname_Class_Faunthorpe               \n",
      "0                         0.383146  890\n",
      "1                         1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Fleming 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Fleming               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Flynn 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Flynn               \n",
      "0                    0.384009  888\n",
      "1                    0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Foley 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Foley               \n",
      "0                    0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Ford 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Ford               \n",
      "0                   0.385569  887\n",
      "1                   0.000000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Fortune 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Fortune               \n",
      "0                      0.383315  887\n",
      "1                      0.500000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Fox 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Surname_Class_Fox              \n",
      "0                  0.38427  890\n",
      "1                  0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Franklin 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Franklin               \n",
      "0                       0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Frauenthal 特征与Survived的关联性:\n",
      "                               生存率  样本数\n",
      "Surname_Class_Frauenthal               \n",
      "0                         0.382452  889\n",
      "1                         1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Frolicher-Stehli 特征与Survived的关联性:\n",
      "                                     生存率  样本数\n",
      "Surname_Class_Frolicher-Stehli               \n",
      "0                               0.383146  890\n",
      "1                               1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Futrelle 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Futrelle               \n",
      "0                       0.383577  889\n",
      "1                       0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Gale 特征与Survived的关联性:\n",
      "                        生存率  样本数\n",
      "Surname_Class_Gale              \n",
      "0                   0.38427  890\n",
      "1                   0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Gibson 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Gibson               \n",
      "0                     0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Giles 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Giles              \n",
      "0                    0.38427  890\n",
      "1                    0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Goldenberg 特征与Survived的关联性:\n",
      "                               生存率  样本数\n",
      "Surname_Class_Goldenberg               \n",
      "0                         0.382452  889\n",
      "1                         1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Goldsmith 特征与Survived的关联性:\n",
      "                              生存率  样本数\n",
      "Surname_Class_Goldsmith               \n",
      "0                        0.382883  888\n",
      "1                        0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Goodwin 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Goodwin               \n",
      "0                      0.386441  885\n",
      "1                      0.000000    6\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Graham 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Graham               \n",
      "0                     0.382883  888\n",
      "1                     0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Greenfield 特征与Survived的关联性:\n",
      "                               生存率  样本数\n",
      "Surname_Class_Greenfield               \n",
      "0                         0.383146  890\n",
      "1                         1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Gustafsson 特征与Survived的关联性:\n",
      "                               生存率  样本数\n",
      "Surname_Class_Gustafsson               \n",
      "0                         0.385569  887\n",
      "1                         0.000000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hagland 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Hagland               \n",
      "0                      0.384702  889\n",
      "1                      0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hakkarainen 特征与Survived的关联性:\n",
      "                                生存率  样本数\n",
      "Surname_Class_Hakkarainen               \n",
      "0                          0.383577  889\n",
      "1                          0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hamalainen 特征与Survived的关联性:\n",
      "                               生存率  样本数\n",
      "Surname_Class_Hamalainen               \n",
      "0                         0.382452  889\n",
      "1                         1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hansen 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Hansen               \n",
      "0                     0.385135  888\n",
      "1                     0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Harder 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Harder               \n",
      "0                     0.383146  890\n",
      "1                     1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Harper 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Harper               \n",
      "0                     0.382187  887\n",
      "1                     0.750000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Harris 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Harris               \n",
      "0                     0.383315  887\n",
      "1                     0.500000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hart 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Hart               \n",
      "0                   0.383315  887\n",
      "1                   0.500000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hays 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Hays               \n",
      "0                   0.382452  889\n",
      "1                   1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Herman 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Herman               \n",
      "0                     0.382452  889\n",
      "1                     1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hickman 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Hickman               \n",
      "0                      0.385135  888\n",
      "1                      0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hippach 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Hippach               \n",
      "0                      0.382452  889\n",
      "1                      1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hirvonen 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Hirvonen               \n",
      "0                       0.383146  890\n",
      "1                       1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hocking 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Hocking               \n",
      "0                      0.383577  889\n",
      "1                      0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hold 特征与Survived的关联性:\n",
      "                        生存率  样本数\n",
      "Surname_Class_Hold              \n",
      "0                   0.38427  890\n",
      "1                   0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Holverson 特征与Survived的关联性:\n",
      "                              生存率  样本数\n",
      "Surname_Class_Holverson               \n",
      "0                        0.383577  889\n",
      "1                        0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Howard 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Howard               \n",
      "0                     0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Hoyt 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Hoyt               \n",
      "0                   0.382883  888\n",
      "1                   0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Ilmakangas 特征与Survived的关联性:\n",
      "                              生存率  样本数\n",
      "Surname_Class_Ilmakangas              \n",
      "0                         0.38427  890\n",
      "1                         0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Jacobsohn 特征与Survived的关联性:\n",
      "                              生存率  样本数\n",
      "Surname_Class_Jacobsohn               \n",
      "0                        0.383577  889\n",
      "1                        0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Jefferys 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Jefferys               \n",
      "0                       0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Jensen 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Jensen               \n",
      "0                     0.385135  888\n",
      "1                     0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Johansson 特征与Survived的关联性:\n",
      "                              生存率  样本数\n",
      "Surname_Class_Johansson               \n",
      "0                        0.385135  888\n",
      "1                        0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Johnson 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Johnson               \n",
      "0                      0.383051  885\n",
      "1                      0.500000    6\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Johnston 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Johnston               \n",
      "0                       0.384702  889\n",
      "1                       0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Jonsson 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Jonsson               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Jussila 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Jussila               \n",
      "0                      0.384009  888\n",
      "1                      0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Kantor 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Kantor               \n",
      "0                     0.383577  889\n",
      "1                     0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Karlsson 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Karlsson              \n",
      "0                       0.38427  890\n",
      "1                       0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Karun 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Karun               \n",
      "0                    0.383146  890\n",
      "1                    1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Keane 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Keane               \n",
      "0                    0.383577  889\n",
      "1                    0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Kelly 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Kelly               \n",
      "0                    0.382187  887\n",
      "1                    0.750000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Kenyon 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Kenyon               \n",
      "0                     0.383146  890\n",
      "1                     1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Khalil 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Khalil               \n",
      "0                     0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Kiernan 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Kiernan              \n",
      "0                      0.38427  890\n",
      "1                      0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Kimball 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Kimball               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Kink 特征与Survived的关联性:\n",
      "                        生存率  样本数\n",
      "Surname_Class_Kink              \n",
      "0                   0.38427  890\n",
      "1                   0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Kink-Heilmann 特征与Survived的关联性:\n",
      "                                  生存率  样本数\n",
      "Surname_Class_Kink-Heilmann               \n",
      "0                            0.383146  890\n",
      "1                            1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Klasen 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Klasen              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Lahtinen 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Lahtinen              \n",
      "0                       0.38427  890\n",
      "1                       0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Lam 特征与Survived的关联性:\n",
      "                        生存率  样本数\n",
      "Surname_Class_Lam               \n",
      "0                  0.383577  889\n",
      "1                  0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Laroche 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Laroche               \n",
      "0                      0.382883  888\n",
      "1                      0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Larsson 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Larsson               \n",
      "0                      0.384702  889\n",
      "1                      0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Lefebre 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Lefebre               \n",
      "0                      0.385569  887\n",
      "1                      0.000000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Lennon 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Lennon              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Lindell 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Lindell              \n",
      "0                      0.38427  890\n",
      "1                      0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Lines 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Lines               \n",
      "0                    0.383146  890\n",
      "1                    1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Lobb 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Lobb               \n",
      "0                   0.384702  889\n",
      "1                   0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Louch 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Louch               \n",
      "0                    0.383146  890\n",
      "1                    1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Mahon 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Mahon               \n",
      "0                    0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Mallet 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Mallet               \n",
      "0                     0.383577  889\n",
      "1                     0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Marvin 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Marvin              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_McCarthy 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_McCarthy              \n",
      "0                       0.38427  890\n",
      "1                       0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_McCoy 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_McCoy               \n",
      "0                    0.382452  889\n",
      "1                    1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_McGowan 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_McGowan               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_McNamee 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_McNamee              \n",
      "0                      0.38427  890\n",
      "1                      0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Mellinger 特征与Survived的关联性:\n",
      "                              生存率  样本数\n",
      "Surname_Class_Mellinger               \n",
      "0                        0.382452  889\n",
      "1                        1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Meyer 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Meyer               \n",
      "0                    0.384009  888\n",
      "1                    0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Minahan 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Minahan               \n",
      "0                      0.383577  889\n",
      "1                      0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Moor 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Moor               \n",
      "0                   0.382452  889\n",
      "1                   1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Moore 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Moore              \n",
      "0                    0.38427  890\n",
      "1                    0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Moran 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Moran               \n",
      "0                    0.384009  888\n",
      "1                    0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Morley 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Morley               \n",
      "0                     0.384702  889\n",
      "1                     0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Moubarek 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Moubarek               \n",
      "0                       0.382452  889\n",
      "1                       1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Murphy 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Murphy               \n",
      "0                     0.382452  889\n",
      "1                     1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Nakid 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Nakid               \n",
      "0                    0.382452  889\n",
      "1                    1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Nasser 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Nasser               \n",
      "0                     0.383577  889\n",
      "1                     0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Navratil 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Navratil               \n",
      "0                       0.382883  888\n",
      "1                       0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Newell 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Newell               \n",
      "0                     0.382883  888\n",
      "1                     0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Nicola-Yarred 特征与Survived的关联性:\n",
      "                                  生存率  样本数\n",
      "Surname_Class_Nicola-Yarred               \n",
      "0                            0.382452  889\n",
      "1                            1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Nilsson 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Nilsson               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_O'Brien 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_O'Brien               \n",
      "0                      0.384009  888\n",
      "1                      0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_O'Connor 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_O'Connor              \n",
      "0                       0.38427  890\n",
      "1                       0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Olsen 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Olsen               \n",
      "0                    0.385135  888\n",
      "1                    0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Olsson 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Olsson               \n",
      "0                     0.384702  889\n",
      "1                     0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Oreskovic 特征与Survived的关联性:\n",
      "                              生存率  样本数\n",
      "Surname_Class_Oreskovic               \n",
      "0                        0.384702  889\n",
      "1                        0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Ostby 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Ostby              \n",
      "0                    0.38427  890\n",
      "1                    0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Palsson 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Palsson               \n",
      "0                      0.385569  887\n",
      "1                      0.000000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Panula 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Panula               \n",
      "0                     0.386441  885\n",
      "1                     0.000000    6\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Peacock 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Peacock               \n",
      "0                      0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Pears 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Pears               \n",
      "0                    0.383577  889\n",
      "1                    0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Penasco y Castellana 特征与Survived的关联性:\n",
      "                                         生存率  样本数\n",
      "Surname_Class_Penasco y Castellana               \n",
      "0                                   0.383577  889\n",
      "1                                   0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Peter 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Peter               \n",
      "0                    0.382452  889\n",
      "1                    1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Petroff 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Petroff               \n",
      "0                      0.384702  889\n",
      "1                      0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Phillips 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Phillips               \n",
      "0                       0.383146  890\n",
      "1                       1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Pokrnic 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Pokrnic               \n",
      "0                      0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Quick 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Quick               \n",
      "0                    0.382452  889\n",
      "1                    1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Rare_Surname 特征与Survived的关联性:\n",
      "                                 生存率  样本数\n",
      "Surname_Class_Rare_Surname               \n",
      "0                           0.434685  444\n",
      "1                           0.333333  447\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Renouf 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Renouf               \n",
      "0                     0.383577  889\n",
      "1                     0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Rice 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Rice               \n",
      "0                   0.386005  886\n",
      "1                   0.000000    5\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Richards 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Richards               \n",
      "0                       0.381757  888\n",
      "1                       1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Risien 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Risien              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Robins 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Robins              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Rogers 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Rogers              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Rosblom 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Rosblom               \n",
      "0                      0.384702  889\n",
      "1                      0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Rothschild 特征与Survived的关联性:\n",
      "                               生存率  样本数\n",
      "Surname_Class_Rothschild               \n",
      "0                         0.383146  890\n",
      "1                         1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Ryan 特征与Survived的关联性:\n",
      "                        生存率  样本数\n",
      "Surname_Class_Ryan              \n",
      "0                   0.38427  890\n",
      "1                   0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Ryerson 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Ryerson               \n",
      "0                      0.382452  889\n",
      "1                      1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Saad 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Saad               \n",
      "0                   0.384702  889\n",
      "1                   0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Sage 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Sage               \n",
      "0                   0.386878  884\n",
      "1                   0.000000    7\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Samaan 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Samaan              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Sandstrom 特征与Survived的关联性:\n",
      "                              生存率  样本数\n",
      "Surname_Class_Sandstrom               \n",
      "0                        0.382452  889\n",
      "1                        1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Silvey 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Silvey               \n",
      "0                     0.383577  889\n",
      "1                     0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Skoog 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Skoog               \n",
      "0                    0.386441  885\n",
      "1                    0.000000    6\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Smith 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Smith               \n",
      "0                    0.384442  887\n",
      "1                    0.250000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Snyder 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Snyder               \n",
      "0                     0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Spedden 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Spedden               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Spencer 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Spencer               \n",
      "0                      0.383146  890\n",
      "1                      1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Stanley 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Stanley               \n",
      "0                      0.383577  889\n",
      "1                      0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Stengel 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Stengel               \n",
      "0                      0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Straus 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Straus               \n",
      "0                     0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Strom 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Strom               \n",
      "0                    0.384702  889\n",
      "1                    0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Svensson 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Svensson               \n",
      "0                       0.384702  889\n",
      "1                       0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Taussig 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Taussig               \n",
      "0                      0.382883  888\n",
      "1                      0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Taylor 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Taylor               \n",
      "0                     0.382452  889\n",
      "1                     1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Thayer 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Thayer               \n",
      "0                     0.382883  888\n",
      "1                     0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Thomas 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Thomas               \n",
      "0                     0.383146  890\n",
      "1                     1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Thorneycroft 特征与Survived的关联性:\n",
      "                                 生存率  样本数\n",
      "Surname_Class_Thorneycroft               \n",
      "0                           0.383577  889\n",
      "1                           0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Touma 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Touma               \n",
      "0                    0.383146  890\n",
      "1                    1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Turpin 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Turpin               \n",
      "0                     0.384702  889\n",
      "1                     0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Van Impe 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Van Impe               \n",
      "0                       0.385135  888\n",
      "1                       0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Vander Planke 特征与Survived的关联性:\n",
      "                                  生存率  样本数\n",
      "Surname_Class_Vander Planke               \n",
      "0                            0.385135  888\n",
      "1                            0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Ware 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Ware               \n",
      "0                   0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Warren 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Warren               \n",
      "0                     0.383146  890\n",
      "1                     1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Watt 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Watt               \n",
      "0                   0.383146  890\n",
      "1                   1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Webber 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Webber               \n",
      "0                     0.383577  889\n",
      "1                     0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Weisz 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Weisz               \n",
      "0                    0.383146  890\n",
      "1                    1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Wells 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Wells               \n",
      "0                    0.383146  890\n",
      "1                    1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_West 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_West               \n",
      "0                   0.382883  888\n",
      "1                   0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_White 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_White               \n",
      "0                    0.384702  889\n",
      "1                    0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Wick 特征与Survived的关联性:\n",
      "                         生存率  样本数\n",
      "Surname_Class_Wick               \n",
      "0                   0.382452  889\n",
      "1                   1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Widener 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Widener              \n",
      "0                      0.38427  890\n",
      "1                      0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Wiklund 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Wiklund              \n",
      "0                      0.38427  890\n",
      "1                      0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Williams 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Williams               \n",
      "0                       0.384442  887\n",
      "1                       0.250000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Wright 特征与Survived的关联性:\n",
      "                          生存率  样本数\n",
      "Surname_Class_Wright              \n",
      "0                     0.38427  890\n",
      "1                     0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Yasbeck 特征与Survived的关联性:\n",
      "                            生存率  样本数\n",
      "Surname_Class_Yasbeck               \n",
      "0                      0.383577  889\n",
      "1                      0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Zabour 特征与Survived的关联性:\n",
      "                           生存率  样本数\n",
      "Surname_Class_Zabour               \n",
      "0                     0.384702  889\n",
      "1                     0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_Zakarian 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_Zakarian               \n",
      "0                       0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_de Messemaeker 特征与Survived的关联性:\n",
      "                                   生存率  样本数\n",
      "Surname_Class_de Messemaeker               \n",
      "0                             0.383146  890\n",
      "1                             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_del Carlo 特征与Survived的关联性:\n",
      "                             生存率  样本数\n",
      "Surname_Class_del Carlo              \n",
      "0                        0.38427  890\n",
      "1                        0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Surname_Class_van Billiard 特征与Survived的关联性:\n",
      "                                生存率  样本数\n",
      "Surname_Class_van Billiard              \n",
      "0                           0.38427  890\n",
      "1                           0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Embarked_Q 特征与Survived的关联性:\n",
      "                 生存率  样本数\n",
      "Embarked_Q               \n",
      "0           0.383292  814\n",
      "1           0.389610   77\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Embarked_S 特征与Survived的关联性:\n",
      "                 生存率  样本数\n",
      "Embarked_S               \n",
      "0           0.502041  245\n",
      "1           0.339009  646\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Pclass_2 特征与Survived的关联性:\n",
      "               生存率  样本数\n",
      "Pclass_2               \n",
      "0.0       0.360679  707\n",
      "1.0       0.472826  184\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Pclass_3 特征与Survived的关联性:\n",
      "               生存率  样本数\n",
      "Pclass_3               \n",
      "0.0       0.557500  400\n",
      "1.0       0.242363  491\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_1 特征与Survived的关联性:\n",
      "                    生存率  样本数\n",
      "Ticket_Group_1              \n",
      "False           0.38427  890\n",
      "True            0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_2 特征与Survived的关联性:\n",
      "                    生存率  样本数\n",
      "Ticket_Group_2              \n",
      "False           0.38427  890\n",
      "True            0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_3 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_3               \n",
      "False           0.383577  889\n",
      "True            0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_4 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_4               \n",
      "False           0.385569  887\n",
      "True            0.000000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_5 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_5               \n",
      "False           0.381757  888\n",
      "True            1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_6 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_6               \n",
      "False           0.383577  889\n",
      "True            0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_7 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_7               \n",
      "False           0.383146  890\n",
      "True            1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_8 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_8               \n",
      "False           0.386878  884\n",
      "True            0.000000    7\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_9 特征与Survived的关联性:\n",
      "                    生存率  样本数\n",
      "Ticket_Group_9              \n",
      "False           0.38427  890\n",
      "True            0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_10 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_10               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_11 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_11               \n",
      "False            0.386005  886\n",
      "True             0.000000    5\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_12 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_12               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_13 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_13               \n",
      "False            0.385135  888\n",
      "True             0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_14 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_14               \n",
      "False            0.383577  889\n",
      "True             0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_15 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_15               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_16 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_16               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_17 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_17               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_18 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_18               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_19 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_19               \n",
      "False            0.382187  887\n",
      "True             0.750000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_20 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_20              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_21 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_21               \n",
      "False            0.383315  887\n",
      "True             0.500000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_22 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_22               \n",
      "False            0.382452  889\n",
      "True             1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_23 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_23              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_24 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_24               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_25 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_25               \n",
      "False            0.383577  889\n",
      "True             0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_26 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_26               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_27 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_27               \n",
      "False            0.382452  889\n",
      "True             1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_28 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_28               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_29 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_29               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_30 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_30               \n",
      "False            0.385135  888\n",
      "True             0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_31 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_31              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_32 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_32               \n",
      "False            0.381757  888\n",
      "True             1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_33 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_33              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_34 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_34               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_35 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_35               \n",
      "False            0.386441  885\n",
      "True             0.000000    6\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_36 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_36               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_37 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_37              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_38 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_38               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_39 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_39              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_40 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_40               \n",
      "False            0.383577  889\n",
      "True             0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_41 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_41               \n",
      "False            0.382452  889\n",
      "True             1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_42 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_42               \n",
      "False            0.383577  889\n",
      "True             0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_43 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_43               \n",
      "False            0.386441  885\n",
      "True             0.000000    6\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_44 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_44               \n",
      "False            0.382187  887\n",
      "True             0.750000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_45 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_45               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_46 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_46              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_47 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_47               \n",
      "False            0.385135  888\n",
      "True             0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_48 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_48               \n",
      "False            0.381222  884\n",
      "True             0.714286    7\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_49 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_49               \n",
      "False            0.385135  888\n",
      "True             0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_50 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_50               \n",
      "False            0.385135  888\n",
      "True             0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_51 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_51              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_52 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_52               \n",
      "False            0.382452  889\n",
      "True             1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_53 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_53               \n",
      "False            0.382452  889\n",
      "True             1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_54 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_54               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_55 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_55               \n",
      "False            0.384442  887\n",
      "True             0.250000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_56 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_56               \n",
      "False            0.382452  889\n",
      "True             1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_57 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_57              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_58 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_58               \n",
      "False            0.383577  889\n",
      "True             0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_59 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_59               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_60 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_60               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_61 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_61               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_62 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_62              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_63 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_63               \n",
      "False            0.382452  889\n",
      "True             1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_64 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_64               \n",
      "False            0.383577  889\n",
      "True             0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_65 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_65               \n",
      "False            0.384009  888\n",
      "True             0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_66 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_66               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_67 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_67              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_68 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_68               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_69 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_69               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_70 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_70               \n",
      "False            0.384009  888\n",
      "True             0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_71 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_71               \n",
      "False            0.385569  887\n",
      "True             0.000000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_72 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_72               \n",
      "False            0.384009  888\n",
      "True             0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_73 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_73               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_74 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_74               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_75 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_75              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_76 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_76               \n",
      "False            0.383747  886\n",
      "True             0.400000    5\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_77 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_77               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_78 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_78              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_79 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_79               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_80 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_80               \n",
      "False            0.383577  889\n",
      "True             0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_81 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_81               \n",
      "False            0.382452  889\n",
      "True             1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_82 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_82               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_83 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_83               \n",
      "False            0.385135  888\n",
      "True             0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_84 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_84               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_85 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_85               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_86 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_86               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_87 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_87               \n",
      "False            0.383146  890\n",
      "True             1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_88 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_88              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_89 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_89              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_90 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_90               \n",
      "False            0.383577  889\n",
      "True             0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_91 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_91               \n",
      "False            0.382883  888\n",
      "True             0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_92 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_92              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_93 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_93               \n",
      "False            0.383577  889\n",
      "True             0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_94 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_94               \n",
      "False            0.382452  889\n",
      "True             1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_95 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_95              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_96 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_96              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_97 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_97               \n",
      "False            0.384702  889\n",
      "True             0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_98 特征与Survived的关联性:\n",
      "                     生存率  样本数\n",
      "Ticket_Group_98              \n",
      "False            0.38427  890\n",
      "True             0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_99 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_99               \n",
      "False            0.382883  888\n",
      "True             0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_100 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_100               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_101 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_101              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_102 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_102              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_103 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_103               \n",
      "False             0.385569  887\n",
      "True              0.000000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_104 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_104              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_105 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_105               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_106 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_106               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_107 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_107              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_108 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_108               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_109 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_109               \n",
      "False             0.381757  888\n",
      "True              1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_110 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_110               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_111 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_111               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_112 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_112               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_113 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_113               \n",
      "False             0.384009  888\n",
      "True              0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_114 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_114              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_115 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_115              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_116 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_116              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_117 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_117               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_118 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_118              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_119 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_119               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_120 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_120               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_121 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_121               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_122 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_122               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_123 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_123               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_124 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_124               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_125 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_125               \n",
      "False             0.382883  888\n",
      "True              0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_126 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_126               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_127 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_127               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_128 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_128              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_129 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_129              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_130 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_130              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_131 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_131               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_132 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_132              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_133 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_133              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_134 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_134              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_135 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_135              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_136 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_136               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_137 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_137               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_138 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_138               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_139 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_139               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_140 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_140               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_141 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_141               \n",
      "False             0.381757  888\n",
      "True              1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_142 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_142               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_143 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_143              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_144 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_144              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_145 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_145               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_146 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_146               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_147 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_147               \n",
      "False             0.381757  888\n",
      "True              1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_148 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_148               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_149 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_149              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_150 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_150               \n",
      "False             0.382883  888\n",
      "True              0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_151 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_151               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_152 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_152              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_153 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_153               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_154 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_154               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_155 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_155               \n",
      "False             0.383315  887\n",
      "True              0.500000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_156 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_156               \n",
      "False             0.381757  888\n",
      "True              1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_157 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_157               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_158 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_158              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_159 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_159               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_160 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_160               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_161 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_161               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_162 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_162               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_163 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_163               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_164 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_164               \n",
      "False             0.383315  887\n",
      "True              0.500000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_165 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_165               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_166 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_166               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_167 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_167               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_168 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_168               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_169 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_169               \n",
      "False             0.382187  887\n",
      "True              0.750000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_170 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_170               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_171 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_171              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_172 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_172               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_173 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_173              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_174 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_174               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_175 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_175               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_176 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_176               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_177 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_177               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_178 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_178              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_179 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_179               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_180 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_180               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_181 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_181              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_182 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_182               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_183 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_183               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_184 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_184              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_185 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_185              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_186 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_186              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_187 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_187              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_188 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_188               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_189 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_189               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_190 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_190               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_191 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_191               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_192 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_192              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_193 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_193              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_194 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_194               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_195 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_195              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_196 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_196               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_197 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_197               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_198 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_198               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_199 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_199               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_200 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_200              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_201 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_201              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_202 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_202              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_203 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_203              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_204 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_204               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_205 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_205               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_206 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_206              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_207 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_207              \n",
      "False             0.38106  887\n",
      "True              1.00000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_208 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_208               \n",
      "False             0.384009  888\n",
      "True              0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_209 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_209               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_210 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_210               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_211 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_211              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_212 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_212               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_213 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_213              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_214 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_214               \n",
      "False             0.384442  887\n",
      "True              0.250000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_215 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_215              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_216 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_216               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_217 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_217               \n",
      "False             0.381757  888\n",
      "True              1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_218 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_218               \n",
      "False             0.385569  887\n",
      "True              0.000000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_219 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_219              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_220 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_220              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_221 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_221               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_222 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_222              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_223 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_223               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_224 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_224              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_225 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_225               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_226 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_226               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_227 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_227               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_228 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_228               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_229 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_229               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_230 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_230               \n",
      "False             0.384009  888\n",
      "True              0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_231 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_231               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_232 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_232               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_233 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_233               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_234 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_234               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_235 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_235               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_236 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_236               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_237 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_237              \n",
      "False             0.38106  887\n",
      "True              1.00000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_238 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_238               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_239 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_239               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_240 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_240              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_241 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_241               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_242 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_242              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_243 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_243              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_244 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_244               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_245 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_245              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_246 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_246              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_247 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_247              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_248 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_248              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_249 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_249              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_250 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_250               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_251 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_251               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_252 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_252              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_253 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_253              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_254 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_254               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_255 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_255              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_256 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_256               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_257 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_257               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_258 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_258              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_259 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_259               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_260 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_260              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_261 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_261               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_262 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_262               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_263 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_263               \n",
      "False             0.384009  888\n",
      "True              0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_264 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_264              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_265 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_265              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_266 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_266              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_267 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_267              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_268 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_268              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_269 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_269              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_270 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_270              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_271 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_271               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_272 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_272               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_273 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_273              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_274 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_274              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_275 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_275               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_276 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_276               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_277 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_277              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_278 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_278               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_279 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_279               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_280 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_280              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_281 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_281              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_282 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_282               \n",
      "False             0.385569  887\n",
      "True              0.000000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_283 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_283               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_284 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_284               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_285 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_285               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_286 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_286               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_287 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_287               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_288 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_288               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_289 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_289              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_290 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_290              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_291 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_291              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_292 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_292               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_293 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_293              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_294 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_294               \n",
      "False             0.382883  888\n",
      "True              0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_295 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_295               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_296 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_296              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_297 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_297               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_298 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_298              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_299 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_299               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_300 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_300               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_301 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_301              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_302 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_302              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_303 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_303               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_304 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_304              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_305 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_305              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_306 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_306              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_307 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_307               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_308 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_308              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_309 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_309               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_310 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_310               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_311 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_311               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_312 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_312               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_313 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_313               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_314 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_314              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_315 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_315               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_316 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_316              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_317 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_317              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_318 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_318              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_319 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_319              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_320 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_320               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_321 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_321               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_322 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_322               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_323 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_323               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_324 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_324              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_325 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_325              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_326 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_326              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_327 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_327              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_328 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_328              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_329 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_329               \n",
      "False             0.381757  888\n",
      "True              1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_330 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_330               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_331 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_331               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_332 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_332               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_333 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_333              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_334 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_334              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_335 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_335               \n",
      "False             0.384009  888\n",
      "True              0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_336 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_336              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_337 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_337               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_338 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_338              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_339 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_339               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_340 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_340              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_341 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_341              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_342 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_342              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_343 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_343               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_344 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_344              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_345 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_345              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_346 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_346               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_347 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_347               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_348 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_348              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_349 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_349              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_350 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_350               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_351 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_351               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_352 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_352              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_353 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_353              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_354 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_354              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_355 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_355              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_356 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_356              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_357 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_357              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_358 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_358              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_359 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_359               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_360 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_360              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_361 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_361              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_362 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_362               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_363 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_363               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_364 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_364               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_365 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_365              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_366 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_366              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_367 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_367              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_368 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_368              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_369 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_369              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_370 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_370               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_371 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_371              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_372 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_372              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_373 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_373               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_374 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_374               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_375 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_375               \n",
      "False             0.385569  887\n",
      "True              0.000000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_376 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_376               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_377 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_377              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_378 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_378              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_379 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_379              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_380 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_380               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_381 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_381              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_382 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_382              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_383 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_383              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_384 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_384               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_385 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_385               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_386 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_386               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_387 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_387               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_388 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_388               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_389 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_389               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_390 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_390               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_391 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_391               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_392 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_392               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_393 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_393               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_394 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_394               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_395 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_395               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_396 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_396               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_397 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_397               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_398 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_398               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_399 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_399               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_400 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_400               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_401 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_401               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_402 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_402               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_403 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_403               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_404 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_404               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_405 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_405               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_406 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_406               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_407 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_407               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_408 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_408               \n",
      "False             0.384009  888\n",
      "True              0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_409 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_409               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_410 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_410               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_411 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_411               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_412 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_412               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_413 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_413               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_414 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_414               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_415 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_415               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_416 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_416               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_417 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_417               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_418 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_418               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_419 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_419               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_420 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_420               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_421 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_421               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_422 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_422               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_423 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_423               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_424 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_424               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_425 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_425               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_426 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_426               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_427 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_427              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_428 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_428               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_429 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_429               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_430 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_430              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_431 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_431               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_432 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_432               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_433 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_433               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_434 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_434               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_435 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_435               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_436 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_436               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_437 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_437               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_438 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_438               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_439 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_439               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_440 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_440               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_441 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_441               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_442 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_442               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_443 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_443               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_444 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_444               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_445 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_445               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_446 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_446               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_447 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_447               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_448 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_448               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_449 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_449               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_450 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_450               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_451 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_451               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_452 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_452               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_453 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_453               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_454 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_454               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_455 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_455               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_456 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_456               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_457 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_457               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_458 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_458               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_459 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_459               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_460 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_460               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_461 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_461               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_462 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_462               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_463 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_463               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_464 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_464               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_465 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_465               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_466 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_466              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_467 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_467               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_468 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_468               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_469 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_469               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_470 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_470               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_471 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_471               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_472 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_472               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_473 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_473               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_474 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_474               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_475 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_475               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_476 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_476               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_477 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_477               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_478 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_478               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_479 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_479               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_480 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_480              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_481 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_481              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_482 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_482              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_483 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_483               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_484 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_484              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_485 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_485              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_486 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_486               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_487 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_487              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_488 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_488               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_489 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_489               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_490 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_490              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_491 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_491              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_492 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_492              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_493 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_493              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_494 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_494               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_495 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_495              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_496 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_496              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_497 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_497               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_498 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_498               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_499 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_499               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_500 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_500              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_501 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_501              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_502 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_502              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_503 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_503              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_504 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_504               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_505 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_505               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_506 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_506              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_507 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_507              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_508 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_508              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_509 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_509              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_510 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_510               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_511 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_511               \n",
      "False             0.384009  888\n",
      "True              0.333333    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_512 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_512              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_513 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_513               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_514 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_514               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_515 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_515               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_516 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_516               \n",
      "False             0.382883  888\n",
      "True              0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_517 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_517               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_518 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_518              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_519 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_519               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_520 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_520              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_521 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_521               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_522 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_522               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_523 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_523              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_524 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_524               \n",
      "False             0.382883  888\n",
      "True              0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_525 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_525              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_526 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_526               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_527 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_527               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_528 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_528              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_529 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_529              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_530 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_530               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_531 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_531              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_532 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_532               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_533 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_533               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_534 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_534               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_535 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_535               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_536 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_536               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_537 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_537               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_538 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_538               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_539 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_539               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_540 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_540              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_541 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_541               \n",
      "False             0.386441  885\n",
      "True              0.000000    6\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_542 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_542               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_543 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_543               \n",
      "False             0.386878  884\n",
      "True              0.000000    7\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_544 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_544               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_545 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_545              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_546 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_546               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_547 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_547               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_548 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_548               \n",
      "False             0.382883  888\n",
      "True              0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_549 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_549               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_550 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_550               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_551 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_551               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_552 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_552              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_553 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_553               \n",
      "False             0.384442  887\n",
      "True              0.250000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_554 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_554               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_555 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_555               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_556 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_556              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_557 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_557               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_558 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_558               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_559 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_559               \n",
      "False             0.381757  888\n",
      "True              1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_560 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_560              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_561 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_561              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_562 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_562               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_563 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_563               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_564 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_564               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_565 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_565              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_566 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_566              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_567 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_567               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_568 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_568               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_569 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_569               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_570 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_570               \n",
      "False             0.381757  888\n",
      "True              1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_571 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_571               \n",
      "False             0.382883  888\n",
      "True              0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_572 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_572               \n",
      "False             0.382883  888\n",
      "True              0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_573 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_573               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_574 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_574               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_575 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_575               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_576 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_576               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_577 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_577               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_578 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_578               \n",
      "False             0.382187  887\n",
      "True              0.750000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_579 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_579              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_580 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_580               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_581 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_581               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_582 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_582              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_583 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_583               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_584 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_584               \n",
      "False             0.381757  888\n",
      "True              1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_585 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_585               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_586 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_586              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_587 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_587               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_588 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_588               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_589 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_589              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_590 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_590               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_591 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_591               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_592 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_592              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_593 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_593               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_594 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_594               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_595 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_595               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_596 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_596              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_597 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_597              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_598 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_598               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_599 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_599              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_600 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_600               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_601 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_601               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_602 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_602               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_603 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_603               \n",
      "False             0.386005  886\n",
      "True              0.000000    5\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_604 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_604              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_605 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_605              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_606 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_606               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_607 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_607               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_608 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_608               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_609 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_609               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_610 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_610              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_611 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_611               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_612 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_612               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_613 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_613              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_614 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_614               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_615 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_615               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_616 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_616               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_617 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_617               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_618 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_618               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_619 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_619               \n",
      "False             0.382883  888\n",
      "True              0.666667    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_620 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_620              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_621 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_621              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_622 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_622               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_623 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_623              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_624 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_624              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_625 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_625               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_626 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_626               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_627 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_627               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_628 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_628               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_629 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_629              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_630 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_630               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_631 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_631               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_632 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_632              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_633 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_633              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_634 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_634               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_635 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_635               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_636 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_636              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_637 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_637              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_638 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_638               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_639 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_639              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_640 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_640               \n",
      "False             0.385135  888\n",
      "True              0.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_641 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_641               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_642 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_642               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_643 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_643               \n",
      "False             0.381757  888\n",
      "True              1.000000    3\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_644 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_644               \n",
      "False             0.382452  889\n",
      "True              1.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_645 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_645               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_646 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_646              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_647 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_647              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_648 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_648               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_649 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_649               \n",
      "False             0.385569  887\n",
      "True              0.000000    4\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_650 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_650              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_651 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_651              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_652 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_652               \n",
      "False             0.383146  890\n",
      "True              1.000000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_653 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_653               \n",
      "False             0.384702  889\n",
      "True              0.000000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_654 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_654               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_655 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_655              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_656 特征与Survived的关联性:\n",
      "                      生存率  样本数\n",
      "Ticket_Group_656              \n",
      "False             0.38427  890\n",
      "True              0.00000    1\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_657 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_657               \n",
      "False             0.383577  889\n",
      "True              0.500000    2\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "Ticket_Group_658 特征与Survived的关联性:\n",
      "                       生存率  样本数\n",
      "Ticket_Group_658               \n",
      "0                 0.383838  891\n",
      "--------------------------------------------------\n",
      "\n",
      "\n",
      "=== 特征与Survived的相关系数 ===\n",
      "Survived              1.000000\n",
      "Sex                   0.543351\n",
      "Name_Class_for_Age    0.494546\n",
      "Pclass_2              0.093349\n",
      "Ticket_Group_207      0.085083\n",
      "                        ...   \n",
      "Ticket_Group_625           NaN\n",
      "Ticket_Group_630           NaN\n",
      "Ticket_Group_631           NaN\n",
      "Ticket_Group_654           NaN\n",
      "Ticket_Group_658           NaN\n",
      "Name: Survived, Length: 906, dtype: float64\n",
      "--------------------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 分析每个特征与Survived的关联性\n",
    "df_with_survived = df_norm.copy()\n",
    "# 将标签数据合并到df_with_survived中\n",
    "df_with_survived['Survived'] = df_tag.values\n",
    "\n",
    "print(\"=== 各特征与Survived关联性分析 ===\\n\")\n",
    "\n",
    "for col in df_with_survived.columns:\n",
    "    if col != 'Survived':\n",
    "        print(f\"{col} 特征与Survived的关联性:\")\n",
    "        # 计算每个特征值的生存率和样本数量\n",
    "        survival_stats = df_with_survived.groupby(col)['Survived'].agg(['mean', 'count'])\n",
    "        survival_stats.columns = ['生存率', '样本数']\n",
    "        print(survival_stats)\n",
    "        print(\"-\" * 50)\n",
    "        print('\\n')\n",
    "\n",
    "# 计算特征与Survived之间的相关系数\n",
    "print(\"=== 特征与Survived的相关系数 ===\")\n",
    "correlation_with_survived = df_with_survived.corr()['Survived'].sort_values(ascending=False)\n",
    "print(correlation_with_survived)\n",
    "print(\"-\" * 50)\n",
    "print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5367bfa5",
   "metadata": {},
   "source": [
    "基于T-SNE的数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3f67b87b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 加载数据\n",
    "X = df_norm  # 高维特征数据\n",
    "y = df_tag.values # 数据标签\n",
    "\n",
    "# 创建t-SNE模型并拟合数据\n",
    "# n_components: 降维后的维度（通常为2）\n",
    "# perplexity: 关键参数，可尝试不同值（如5, 30, 50）\n",
    "# random_state: 设置随机种子使结果可复现\n",
    "tsne = TSNE(n_components=2, perplexity=55, random_state=40)\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "# 可视化结果\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y, cmap='Set1', s=20)\n",
    "plt.colorbar()\n",
    "plt.title('t-SNE Visualization of titanic Digits')\n",
    "plt.xlabel('t-SNE component 1')\n",
    "plt.ylabel('t-SNE component 2')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "990dd378",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练集与验证集准备\n",
    "# 采用pclass+sex的双重分组结构\n",
    "# 1、数据先按sex进行分组，然后对分出的两组按pclass进行分组，一共分成6组。\n",
    "# 2、对每组数据进行0.8的训练集和0.2的验证集\n",
    "# 3、然后将训练集和验证集各自合并后分别保存为train_prc.csv和val_prc.csv\n",
    "#x_train,x_val,y_train,y_val = train_test_split(df_norm, df_tag,test_size=0.2, random_state=42)\n",
    "\n",
    "df_features = df_norm  # 使用已归一化的特征数据\n",
    "df_labels = df_tag     # 标签数据\n",
    "\n",
    "# 按Sex和Pclass分组，共6组: (male/female) × (1/2/3)\n",
    "groups = df_selected.groupby(['Sex', 'Pclass'])\n",
    "\n",
    "# 存储各组的训练集和验证集\n",
    "train_groups = []\n",
    "train_tag_groups = []\n",
    "val_groups = []\n",
    "val_tag_groups = []\n",
    "\n",
    "# 对每组数据进行训练集和验证集划分\n",
    "for name, group in groups:\n",
    "    # 获取这组数据的索引\n",
    "    group_indices = group.index\n",
    "    \n",
    "    # 根据索引获取对应的特征和标签数据\n",
    "    group_features = df_features.loc[group_indices]\n",
    "    group_labels = df_labels.loc[group_indices]\n",
    "    \n",
    "    # 如果组内样本数太少，可能需要特殊处理\n",
    "    if len(group) > 1:\n",
    "        # 按索引进行训练集和验证集划分\n",
    "        X_train, X_val, y_train, y_val = train_test_split(\n",
    "            group_features, group_labels, \n",
    "            test_size=0.2, \n",
    "            random_state=42\n",
    "        )\n",
    "        \n",
    "        train_groups.append(X_train)\n",
    "        train_tag_groups.append(y_train)\n",
    "        val_groups.append(X_val)\n",
    "        val_tag_groups.append(y_val)\n",
    "    else:\n",
    "        # 如果只有一个样本，直接放入训练集\n",
    "        train_groups.append(group_features)\n",
    "        train_tag_groups.append(group_labels)\n",
    "        # 空的验证集\n",
    "        empty_features = pd.DataFrame(columns=group_features.columns)\n",
    "        empty_labels = pd.DataFrame(columns=group_labels.columns)\n",
    "        val_groups.append(empty_features)\n",
    "        val_tag_groups.append(empty_labels)\n",
    "\n",
    "# 合并所有训练集和验证集\n",
    "train_prc = pd.concat(train_groups, ignore_index=True)\n",
    "train_prc_tag = pd.concat(train_tag_groups, ignore_index=True)\n",
    "val_prc = pd.concat([group for group in val_groups if len(group) > 0], ignore_index=True)\n",
    "val_prc_tag = pd.concat([group for group in val_tag_groups if len(group) > 0], ignore_index=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7c425f27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建t-SNE模型并拟合数据\n",
    "# n_components: 降维后的维度（通常为2）\n",
    "# perplexity: 关键参数，可尝试不同值（如5, 30, 50）\n",
    "# random_state: 设置随机种子使结果可复现\n",
    "tsne = TSNE(n_components=2, perplexity=55, random_state=40)\n",
    "train_tsne = tsne.fit_transform(train_prc)\n",
    "val_tsne = tsne.fit_transform(val_prc)\n",
    "\n",
    "\n",
    "# 可视化结果\n",
    "plt.figure(figsize=(14, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(train_tsne[:, 0], train_tsne[:, 1], c=train_prc_tag.values, cmap='Set1', s=20)\n",
    "plt.colorbar()\n",
    "plt.title('t-SNE Visualization of train')\n",
    "plt.xlabel('t-SNE component 1')\n",
    "plt.ylabel('t-SNE component 2')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(val_tsne[:, 0], val_tsne[:, 1], c=val_prc_tag.values, cmap='Set1', s=20)\n",
    "plt.colorbar()\n",
    "plt.title('t-SNE Visualization of val')\n",
    "plt.xlabel('t-SNE component 1')\n",
    "plt.ylabel('t-SNE component 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c4abe641",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据保存完成。\n"
     ]
    }
   ],
   "source": [
    "\n",
    "train_prc.to_csv('data/train_prc.csv', index=False)\n",
    "train_prc_tag.to_csv('data/train_tag.csv', index=False)\n",
    "val_prc.to_csv('data/val_prc.csv', index=False)\n",
    "val_prc_tag.to_csv('data/val_tag.csv', index=False)\n",
    "df_norm_test.to_csv('data/test_prc.csv', index=False)\n",
    "print(\"数据保存完成。\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "kaggle",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
